Harnessing the Hubble Space Telescope Archives: A Catalogue of 21,926 Interacting Galaxies

TL;DR

This paper presents a large, new catalogue of 21,926 interacting galaxies identified from the Hubble Space Telescope archives using neural networks, significantly expanding available data for galaxy evolution studies.

Contribution

The authors develop a novel approach combining neural networks and visual analysis to identify a large sample of interacting galaxies from HST data, surpassing previous catalogues in size.

Findings

Catalogue contains 21,926 interacting galaxy systems, mostly at z<1.

65% of these systems are newly identified, not in existing databases.

The method also discovers other objects like gravitational lenses and protoplanetary disks.

Abstract

Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems require ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalogue of interacting galaxies from the Hubble Space Telescope science archives; this catalogue is larger than previously published catalogues by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST $F814W$ images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalogue. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with $z < 1$. Sixty five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and `backlit' overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalogue publicly available for use by the community. In addition to providing a new catalogue of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.